def save(name, ext, target, sep=",", dec=".", rem=[], hidden=[100], lr=0.1, pat=5, d=1, l="mse", act="tanh", e=200, bsize=100, verb=0, ts=.2): ds = Dataset("{}.{}".format(name, ext), target, sep, dec, rem, testSize=ts) # Split the data Xtrain, Xvalid, yTrain, yValid = train_test_split(ds.Xtrain, ds.ytrain.values, test_size=0.1, shuffle=True) model = Regressor(hidden=hidden, lr=lr, pat=pat, delta=d, loss=l, act=act) model.fit(Xtrain, yTrain, Xvalid, yValid, ep=e, bs=bsize, v=verb) if verb > 0: model.plot() print("Metrics for", name, "data set:") model.metrics(ds.Xtest, ds.ytest.values) print("Saving model...") model.save(name) if verb > 0: return ds print()